MACHINE LEARNING-BASED OPTIMIZATION OF AIRFARE PRICE FORECASTING

Authors

  • Kammari Supriya Student Department of CSE St Johns College Of Engineering And Technology, Yemmiganur, Kurnool, AP Author
  • Gandla Nagappa Associate professor Department of CSE St Johns College Of Engineering And Technology, Yemmiganur, Kurnool, AP Author

DOI:

https://doi.org/10.48047/wwg0qk10

Keywords:

.

Abstract

Accurate airfare price forecasting is essential for both travelers and airline companies, enabling cost-effective bookings and optimized revenue management. Airfare prices are highly dynamic, influenced by factors such as seasonality, demand fluctuations, airline competition, and fuel costs. Conventional price prediction methods struggle to adapt to these complex variations, often leading to inaccurate forecasts.

Downloads

Download data is not yet available.

References

Concept of a Numerical Forecast Model. Accessed: Aug. 10, 2023. [Online]. Available: http://web.kma.go.kr/aboutkma/intro/superc

om/model/model_concept.jsp

P. Davis, C. Ruth, A. A. Scaife, and J. Kettleborough, ‘‘A large ensemble seasonal forecasting system: GloSea6,’’ Dec. 2020,

vol. 2020.

Downloads

Published

2025-03-10

How to Cite

Supriya, K., & Nagappa, G. . (2025). MACHINE LEARNING-BASED OPTIMIZATION OF AIRFARE PRICE FORECASTING. Cuestiones De Fisioterapia, 54(5), 1-9. https://doi.org/10.48047/wwg0qk10